1. Scope of Work

This is an example of what the automated proteomics analysis pipeline outputs, using all synthetic dummy data as an example. Typically, a text file is read in that details the scope of work, sample prep, and data acquisition - which has all been omitted for this example. This is generally used for core reports but also used to start research studies as a baseline analysis.

  • Sample Type: Water

  • Sample #: 00

  • Type of analysis: Proteomics

  • Instrument: Instrument 1

2. Sample Preparation

  • Protein was digested using….
  • Peptides were processed using…

3. Data Acquisition

  • MS data were acquired on…
  • Peptides were directly loaded on…
  • MS settings:
    • MS1 scans…
    • MS2 scans…
    • Only precursors with…
    • Resampling of same precursors was…

4. Search Parameters

  • Global proteomics data were searched using…
    • Digestion
    • Peptide modifications
    • Missed cleavages
    • FDR threshold

5. Samples

The following table was taken directly from the sample submisison sheet, detailing the samples included in this study.
Sample ID Sample Type Species Condition 1 Condition 2
1 Tissue Human Vehicle Test
2 Tissue Human Vehicle Test
3 Tissue Human 1mg Test
4 Tissue Human 1mg Test
5 Tissue Human 1mg Test
6 Tissue Human 5mg Test
7 Tissue Human 5mg Test
8 Tissue Human 5mg Test
9 Tissue Human Vehicle Test
10 Tissue Human Vehicle Test
11 Tissue Human Vehicle Control
12 Tissue Human Vehicle Control
13 Tissue Human 1mg Control
14 Tissue Human 1mg Control
15 Tissue Human 1mg Control
16 Tissue Human 5mg Control
17 Tissue Human 5mg Control
18 Tissue Human Vehicle Control
19 Tissue Human Vehicle Control

6. Quality Control

Samples were bracketed by E. coli QC runs, which were then correlated to ensure instrument quality. QC passed threshold (>= 0.9) with an average R^2 of 0.98

TEV Protein Spike

TEV protein has been spiked into all samples as a quality control measure to ensure consistency in sample preparation and instrument performance across all samples.

7. Data Missingness

The bar plot below shows the percent missingness in each sample relative to the total amount of unique proteins identified across all samples. Missingness is expected, particularly when working with different conditions between samples. Any sample with more than 50% missingness is flagged.

8. Protein Identification

9. Data Analysis

Unsupervised clustering of protein abundances

Heatmap

PCA Plots

Unique Proteins

The upset plot below visualizes the proteins intersecting across different conditions, highlighting unique proteins identified within the groups.

Differentially Abundant Proteins

Volcano Plots

Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 5mg_PS19 and Vehicle_PS19 (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
  • 108 proteins are significantly increased in 5mg_PS19
  • 125 proteins are significantly decreased in 5mg_PS19



Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 1mg_PS19 and Vehicle_PS19 (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
  • 108 proteins are significantly increased in 1mg_PS19
  • 125 proteins are significantly decreased in 1mg_PS19



Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 5mg_PS19 and 1mg_PS19 (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
  • 108 proteins are significantly increased in 5mg_PS19
  • 125 proteins are significantly decreased in 5mg_PS19



Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between Vehicle_PS19 and Vehicle_NonTg (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
  • 108 proteins are significantly increased in Vehicle_PS19
  • 125 proteins are significantly decreased in Vehicle_PS19



Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 5mg_PS19 and Vehicle_NonTg (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
  • 108 proteins are significantly increased in 5mg_PS19
  • 125 proteins are significantly decreased in 5mg_PS19



Welch's t-test was used to assess differential abundance, identifying 233 proteins as significantly different between 1mg_PS19 and Vehicle_NonTg (p<0.05). Of which 0 proteins remained significant after controlling for the false discovery rate using the Benjamini-Hochberg method (q < 0.05)
  • 108 proteins are significantly increased in 1mg_PS19
  • 125 proteins are significantly decreased in 1mg_PS19



Acknowledgements

IMS Acknowledgments & Co-authorship Guidelines

All work performed at the Integrated Mass Spectrometry (IMS) at City of Hope’s Comprehensive Cancer Center should be acknowledged in scholarly reports, presentations, posters, papers, and all other publications. Proper acknowledgment provides a visible measure of the impact of the City of Hope’s shared resources and is essential for our continued funding.

When to Acknowledge or Provide Co-Authorship

Include an acknowledgement any time IMS provides services that support your research.

If staff members have made a significant intellectual contribution beyond routine sample analysis, co-authorship is expected. We determine co-authorship based on authorship guidelines published by the Association of Biomolecular Resource Facilities (ABRF).

Format for Co-Author Affiliations

Please acknowledge staff members as “Integrated Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center, Duarte, CA”

Format for Manuscript Acknowledgments

Include the following statement, as required by the NCI:

“We acknowledge the support of the IMS at City of Hope Comprehensive Cancer Center supported by the National Cancer Institute of the National Institutes of Health under award number P30CA33572.”

Notify Facility of Acknowledgement

Please notify IMS when your scholarly report, presentation, poster, or paper containing a facility acknowledgement is published. Accurately quantifying the impact of our facility helps to keep the facility open and available to collaborators.

NIH and NSF Grant Attribution

Please connect with the facility director for language on how our facilities may be described in grant proposals to both the National Institutes of Health and the National Science Foundation.

Data Retention

Raw and processed data are securely stored and retained for 30 calendar days post-project completion. Collaborators will be provided with a downloadable link for data access within this period after which data will be archived. Archived data may be retrieved up to seven years, for a fee.

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